{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 为什么用收益率而不是价格\n",
    "业界流行的技术分析是基于资产的价格数据来进行分析\n",
    "\n",
    "学术界，金融时间序列分析，通常是基于收益率数据的，而不是基于价格数据\n",
    "\n",
    "主要原因是可以假设收益率数据是平稳的（Stationary），而价格数据是不平稳得。\n",
    "\n",
    "**平稳性**是金融时间序列分析中一个非常重要得概念。\n",
    "\n",
    "平稳得时间序列有着更好得统计特性，更便于建模分析。\n",
    "\n",
    "平稳性，直观地理解，就是时间序列的统计特性不随着时间的变化而变化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2举例：针对对数收益率进行分析，先使用TuShare获取沪深300指数2016年的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>3725.86</td>\n",
       "      <td>3470.41</td>\n",
       "      <td>3726.25</td>\n",
       "      <td>3469.01</td>\n",
       "      <td>115370674.0</td>\n",
       "      <td>sz399300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>3382.18</td>\n",
       "      <td>3478.78</td>\n",
       "      <td>3518.22</td>\n",
       "      <td>3377.28</td>\n",
       "      <td>162116984.0</td>\n",
       "      <td>sz399300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>3482.41</td>\n",
       "      <td>3539.81</td>\n",
       "      <td>3543.74</td>\n",
       "      <td>3468.47</td>\n",
       "      <td>145966144.0</td>\n",
       "      <td>sz399300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3294.38</td>\n",
       "      <td>3481.15</td>\n",
       "      <td>3284.74</td>\n",
       "      <td>44102641.0</td>\n",
       "      <td>sz399300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>3371.87</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3418.85</td>\n",
       "      <td>3237.93</td>\n",
       "      <td>185959451.0</td>\n",
       "      <td>sz399300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     open    close     high      low       volume      code\n",
       "0  2016-01-04  3725.86  3470.41  3726.25  3469.01  115370674.0  sz399300\n",
       "1  2016-01-05  3382.18  3478.78  3518.22  3377.28  162116984.0  sz399300\n",
       "2  2016-01-06  3482.41  3539.81  3543.74  3468.47  145966144.0  sz399300\n",
       "3  2016-01-07  3481.15  3294.38  3481.15  3284.74   44102641.0  sz399300\n",
       "4  2016-01-08  3371.87  3361.56  3418.85  3237.93  185959451.0  sz399300"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "df = ts.get_k_data('399300', index=True, start='2016-01-01', end='2016-12-31')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用close数据，并基于close计算对数收益率即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>rtn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>3371.87</td>\n",
       "      <td>3361.56</td>\n",
       "      <td>3418.85</td>\n",
       "      <td>3237.93</td>\n",
       "      <td>185959451.0</td>\n",
       "      <td>sz399300</td>\n",
       "      <td>8.117748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>3303.12</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>3342.48</td>\n",
       "      <td>3192.45</td>\n",
       "      <td>174638387.0</td>\n",
       "      <td>sz399300</td>\n",
       "      <td>8.065997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>3214.82</td>\n",
       "      <td>3215.71</td>\n",
       "      <td>3242.25</td>\n",
       "      <td>3174.55</td>\n",
       "      <td>128225796.0</td>\n",
       "      <td>sz399300</td>\n",
       "      <td>8.073291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>3240.48</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>3257.29</td>\n",
       "      <td>3155.88</td>\n",
       "      <td>120666494.0</td>\n",
       "      <td>sz399300</td>\n",
       "      <td>8.054460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>3076.65</td>\n",
       "      <td>3221.57</td>\n",
       "      <td>3226.66</td>\n",
       "      <td>3072.04</td>\n",
       "      <td>134537671.0</td>\n",
       "      <td>sz399300</td>\n",
       "      <td>8.075120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date     open    close     high      low       volume      code  \\\n",
       "4  2016-01-08  3371.87  3361.56  3418.85  3237.93  185959451.0  sz399300   \n",
       "5  2016-01-11  3303.12  3192.45  3342.48  3192.45  174638387.0  sz399300   \n",
       "6  2016-01-12  3214.82  3215.71  3242.25  3174.55  128225796.0  sz399300   \n",
       "7  2016-01-13  3240.48  3155.88  3257.29  3155.88  120666494.0  sz399300   \n",
       "8  2016-01-14  3076.65  3221.57  3226.66  3072.04  134537671.0  sz399300   \n",
       "\n",
       "        rtn  \n",
       "4  8.117748  \n",
       "5  8.065997  \n",
       "6  8.073291  \n",
       "7  8.054460  \n",
       "8  8.075120  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['rtn'] = np.log(df['close'] - np.log(df['close'].shift(1)))\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28826afc208>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "date = df['date']\n",
    "rtn = df['rtn']\n",
    "close = df['close']\n",
    "plt.plot(date, close)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28826dd0710>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(date, rtn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 平稳性\n",
    "对于一个随机过程，如果随着时间的变化，其表现得各个统计特性都不变，则称这个随机过程具有**强平稳性**。\n",
    "\n",
    "在数学上，强平稳性是这样定义的：对于时间序列${r_1}$，$r_1,r_2,...,r_n$的分布与$r_{1+m},r_{2+m},...,r_{n+m}$的分布相同。\n",
    "\n",
    "即，这个n个观测序列的概率分布不取决于它们的初始时间。\n",
    "\n",
    "**强平稳性**是一个很强的假设，因为它需要所有的统计特性在时间上保持不变。\n",
    "\n",
    "一般来说，会假设金融序列是**弱平稳性的**\n",
    "\n",
    "**弱平稳性**并不要求所有的统计特性一直保持不变，只要求均值、方差以及协方差随着时间变化不发生改变即可。\n",
    "\n",
    "**平稳性**是很多时间序列模型的基础。也就是说，在使用这些模型之前，一定要检验数据是否平稳。否则，建模的结果就是不可信的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 白噪声序列\n",
    "时间序列${r_t}$如果具有**有限均值**和**有限方差**，并且是**独立同分布**随机变量序列，那么就称${r_t}$为**白噪声序列**。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 自相关系数\n",
    "**相关系数**是用来衡量两个随机变量的线性相关性和相关程度的。\n",
    "\n",
    "计算相关系数的公式为：\n",
    "\n",
    "$\\rho_{x,y} = \\frac{Cov(X,Y)}{\\sqrt{Var(X)Var(Y)}}$\n",
    "\n",
    "在时间序列分析中，相关的一个重要概念是**自相关函数（Autocorrelation Function, ACF)**\n",
    "\n",
    "当一个弱平稳收益率序列$r_t$，当与它的过去值$r_{t-l}$是线性相关关系时，就可以将相关系数的概念推广到自相关系数。\n",
    "\n",
    "$r_t$与$r_{t-l}$的相关系数称为$r_t$的间隔为l的自相关系数，通常记为$\\rho_l$，在弱平稳性的假设下，它只是l的函数。具体定义为：\n",
    "\n",
    "$\\rho_l = \\frac{Cov(r_t,r_{t-l})}{\\sqrt{Var(r_t)Var(r_{t-l})}} = \\frac{Cov(r_t,r_{t-l})}{\\sqrt{Var(r_t)Var(r_{t-l})}} = \\frac{\\gamma_l}{\\gamma_0}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制收益率的ACF图\n",
    "import statsmodels.api as sm\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "axl = fig.add_subplot(111)\n",
    "fig = sm.graphics.tsa.plot_acf(df['rtn'], ax = axl, lags = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我感觉上面画的有问题，没有计算ACF的过程啊？难道是封装在了plot_acf函数里面？但是画出的图和书上不一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 混成检验\n",
    "很多时候，我们倾向认为通过历史收益率是很难预测将来的收益率的，即收益率的时间序列自相关系数为0.\n",
    "\n",
    "为了验证这个结论，需要使用相应的统计检验手段。\n",
    "\n",
    "**混成检验（Portmanteau Test）**经常用来检验$r_t$的几个相关系数是否同时为0.\n",
    "\n",
    "\n",
    "原假设：$H_0: \\rho_1 = ... = \\rho_m = 0$\n",
    "\n",
    "备择假设：$H_a$: 对某$i ∈ {1,...,m}, \\rho_i ≠ 0$\n",
    "\n",
    "混成检验统计量：$Q^*(m) = T(T+2)\\sum_{i=1}^{m}\\frac{\\hat{\\rho_l}^2}{T-l}$\n",
    "\n",
    "决策规则是：$Q(m) > \\chi^2_a$，拒绝原假设$H_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AC</th>\n",
       "      <th>Q</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>-0.178514</td>\n",
       "      <td>7.839777</td>\n",
       "      <td>0.005111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.156326</td>\n",
       "      <td>13.876707</td>\n",
       "      <td>0.000970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>-0.099271</td>\n",
       "      <td>16.321298</td>\n",
       "      <td>0.000974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.056385</td>\n",
       "      <td>17.113263</td>\n",
       "      <td>0.001837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>-0.166990</td>\n",
       "      <td>24.088764</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>-0.026627</td>\n",
       "      <td>24.266859</td>\n",
       "      <td>0.000466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.002090</td>\n",
       "      <td>24.267962</td>\n",
       "      <td>0.001022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.000263</td>\n",
       "      <td>24.267979</td>\n",
       "      <td>0.002066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.116595</td>\n",
       "      <td>27.726689</td>\n",
       "      <td>0.001059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>-0.006233</td>\n",
       "      <td>27.736615</td>\n",
       "      <td>0.001989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AC          Q   P-value\n",
       "lag                                \n",
       "1.0  -0.178514   7.839777  0.005111\n",
       "2.0   0.156326  13.876707  0.000970\n",
       "3.0  -0.099271  16.321298  0.000974\n",
       "4.0   0.056385  17.113263  0.001837\n",
       "5.0  -0.166990  24.088764  0.000209\n",
       "6.0  -0.026627  24.266859  0.000466\n",
       "7.0   0.002090  24.267962  0.001022\n",
       "8.0   0.000263  24.267979  0.002066\n",
       "9.0   0.116595  27.726689  0.001059\n",
       "10.0 -0.006233  27.736615  0.001989"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以下是对沪深300指数进行混成检验的代码\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# 获取数据\n",
    "df = ts.get_k_data('399300', index = True, start = '2016-01-01', end = '2016-12-31')\n",
    "df = df.set_index('date')\n",
    "\n",
    "# 计算收益率\n",
    "df['rtn'] = np.log(df['close']) - np.log(df['close'].shift(1))\n",
    "df = df.dropna()\n",
    "\n",
    "# 检验10个自相关系数\n",
    "m = 10\n",
    "acf, q, p = sm.tsa.acf(df['rtn'], nlags = m, qstat = True)\n",
    "out = np.c_[range(1, 11), acf[1:], q, p]\n",
    "output = pd.DataFrame(out, columns = ['lag', 'AC', 'Q', 'P-value'])\n",
    "output = output.set_index('lag')\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. ARMA模型\n",
    "ARMA模型是AR模型和MA模型的一种结合形式\n",
    "\n",
    "#### 9.1 MA模型\n",
    "MA(q)模型的表达公式如下：$r_t = c_0 + a_t - \\theta_1{a_{t-l}} - ...- \\theta_q{a_{t-q}}$\n",
    "\n",
    "其中，$c_0$是一个常数，{a_t}是一个白噪声序列\n",
    "\n",
    "Python并没有为MA模型专门提供模型，如果需要拟合MA模型，只需要使用ARMA模型，将$\\rho$置为0即可。\n",
    "\n",
    "下面是一个例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 先使用ACF函数判断阶次q\n",
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = ts.get_k_data('399300', index = True, start = '2015-01-01', end = '2015-12-31')\n",
    "df = df.set_index('date')\n",
    "df['rtn'] = np.log(df['close']) - np.log(df['close'].shift(1))\n",
    "df = df.dropna()\n",
    "\n",
    "fig = plt.figure(figsize= (10, 5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "fig = sm.graphics.tsa.plot_acf(df.rtn, ax = ax1, lags = 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在10之前存在大量的高自相关系数，虽然21点也超过了，但10到21之间的点都没有超过，可以认为21是意外情况，10才是临界值。所以可以假定阶次q为10。\n",
    "接下来，使用MA模型拟合，并且查看预测效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2882976b940>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rtn = df.rtn.values\n",
    "# 测试集长度\n",
    "t_len = 20\n",
    "# 测试集\n",
    "train = rtn[:-t_len]\n",
    "# 训练集\n",
    "test = rtn[-t_len:]\n",
    "\n",
    "# 拟合模型\n",
    "order = (0, 10)\n",
    "ma_model = sm.tsa.ARMA(train, order).fit()\n",
    "\n",
    "# 获取对应的预测值\n",
    "predicts = ma_model.predict(len(rtn) - t_len, len(rtn) - 1, dynamic = True)\n",
    "\n",
    "comp = pd.DataFrame()\n",
    "comp['original'] = test\n",
    "comp['predict'] = predicts\n",
    "comp.plot(figsize = (10, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MA模型过于简单，一般不会产生有效的拟合和预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9.2 ARMA模型公式\n",
    "使用高阶的AR或者MA模型描述数据的动态结构时，会有很多参数需要估计。为了克服这个困难，人们提出了自回归滑动平均（ARMA）模型。\n",
    "\n",
    "该模型的基本思想是将AR和MA模型的想法结合在一个紧凑的形式中，使所使用参数的个数保持很小。\n",
    "\n",
    "ARMA模型的概念与波动率建模存在密切关系。事实上，广义的自回归条件异方差（GARCH）模型就可以认为使${a^2_t}得ARMA模型。\n",
    "\n",
    "ARMA(p,q)模型得表达形式如下：\n",
    "\n",
    "$r_t = \\Phi_0 + \\sum_{i=1}^{p}(\\Phi_i)(r_{t-l}) + a_t - \\sum_{i=1}^{q}(\\theta_i)(a_{t-i})$\n",
    "\n",
    "其中，${a_t}$是白噪声序列，p和q都是非负整数。AR和MA模型是ARMA(p,q)得特殊情形。\n",
    "\n",
    "当q=0得时候，该模型就是$AR(\\rho)$模型\n",
    "\n",
    "当q=0的时候，该模型就是$MA(q)$模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9.3 ARMA模型阶次判定\n",
    "一般来说，判断$\\rho$要使用PACF函数，判断q要使用ACF函数。\n",
    "\n",
    "同时绘制PACF和ACF图形，示例代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = ts.get_k_data('399300', index = True, start = '2015-01-01', end = '2015-12-31')\n",
    "df = df.set_index('date')\n",
    "df['rtn'] = np.log(df['close']) - np.log(df['close'].shift(1))\n",
    "df = df.dropna()\n",
    "\n",
    "fig = plt.figure(figsize = (10, 8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(df.rtn, lags = 30, ax = ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(df.rtn, lags = 30, ax = ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 再用信息准则判断一下\n",
    "print(sm.tsa.arma_order_select_ic(df.rtn.values, max_ar = 10, max_ma = 10, ic = 'aic')['aic_min_order'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9.4 建立ARMA模型\n",
    "用9.3节计算出来的阶次(2,3)来建立ARMA模型，并利用拟合的模型进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x270f5c589e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rtn = df.rtn.values\n",
    "\n",
    "# 测试集长度\n",
    "t_len = 20\n",
    "\n",
    "# 测试集\n",
    "train = rtn[:-t_len]\n",
    "\n",
    "# 训练集\n",
    "test = rtn[-t_len:]\n",
    "\n",
    "# 拟合模型\n",
    "order = (2,3)\n",
    "arma_model = sm.tsa.ARMA(train, order).fit()\n",
    "\n",
    "# 获取对应的预测值\n",
    "predicts = arma_model.predict(len(rtn) - t_len, len(rtn) - 1, dynamic = True)\n",
    "\n",
    "comp = pd.DataFrame()\n",
    "comp['original'] = test\n",
    "comp['predict'] = predicts\n",
    "comp.plot(figsize = (10, 5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
